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- Cebecauer, Matej, et al.
(författare)
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Revealing representative day-types in transport networks using traffic data clustering
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Recognition of spatio-temporal traffic patterns at the network-wide level plays an important role in data-driven intelligent transport systems (ITS) and is a basis for applications such as short-term prediction and scenario-based traffic management. Common practice in the transport literature is to rely on well-known general unsupervised machine-learning methods (e.g., k-means, hierarchical, spectral, DBSCAN) to select the most representative structure and number of day-types based solely on internal evaluation indices. These are easy to calculate but are limited since they only use information in the clustered dataset itself. In addition, the quality of clustering should ideally be demonstrated by external validation criteria, by expert assessment or the performance in its intended application. The main contribution of this paper is to test and compare the common practice of internal validation with external validation criteria represented by the application to short-term prediction, which also serves as a proxy for more general traffic management applications. When compared to external evaluation using short-term prediction, internal evaluation methods have a tendency to underestimate the number of representative day-types needed for the application. Additionally, the paper investigates the impact of using dimensionality reduction. By using just 0.1\% of the original dataset dimensions, very similar clustering and prediction performance can be achieved, with up to 20 times lower computational costs, depending on the clustering method. K-means and agglomerative clustering may be the most scalable methods, using up to 60 times fewer computational resources for very similar prediction performance to the p-median clustering.
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| 2. |
- Cebecauer, Matej, et al.
(författare)
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Similarity and Interchangeability of Flow and Speed Data for Transport Network Day-Type Clustering and Prediction
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Prediction of future traffic states is an essential part of traffic management and intelligent transportation systems. Previous work has shown that spatio-temporal clustering of traffic data such as flows or speeds into network day-types improves both the performance and the robustness of traffic predictions. Since some data types may not be available at a network-wide level, or only for certain periods, this paper investigates how similar such representative day-types are if based on different data types. The similarity of day-type clusters is evaluated with qualitative calendar visualization and two quantitative metrics, the Adjusted Mutual Information (AMI) which considers day-to-cluster assignments, and a new proposed Centroids Similarity Score (CSS) which compares centroids. The paper also explores the impact on flow and speed prediction performance of substituting one data type for the other in the clustering or classification phases. Using microwave sensor data from the Stockholm motorway network, our findings show that clusterings based on flows and speeds and across a range of clustering methods have reasonably high similarity. CSS is found to be a more relevant similarity indicator than AMI in the prediction application context. By capturing more relevant traffic state information, flow-based clustering and classification are robust for both flow and speed predictions, while speed-based clustering significantly degrades flow prediction performance.
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